Alleviating controversiality problem in trust estimation

Trustworthiness of users has become an indispensable aspect of security in networks. Not having an experience of interacting among nodes is a challenge for this phenomenon. Trust Management(TM) systems brought into play to bring security to any kind of network. Many web-based social networks assign users unique scores based on their interaction with their neighbors. Others, however, argue that trustworthiness of a user varies according to the different views of different users, and consider the so-called local trust metrics. Due to the existence of controversial users, users who are trusted and distrusted by many, in any social community, we have to consider other facts to estimate trusts with better accuracy. There is a strong bond between sociology and trust. Therefore, in this paper, we propose PredSimTrust, a trust estimation algorithm which estimates trust of a target user based on the similarities and trusts of predecessors. We examined the proposed algorithm on a real user community. The results show that our algorithm outperforms other models such as Iterative Multiplication strategy (IMS) and other models which take just one of these notions (trust value and similarity) into consideration.